Overview
Explore DevOps principles for effective collaboration between data scientists and software engineers in machine learning projects, focusing on automated pipelines and best practices.
Syllabus
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- **Introduction to DevOps and Machine Learning**
-- Overview of DevOps principles and practices
-- The role of DevOps in machine learning projects
-- Differences between traditional DevOps and MLOps
- **Setting Up the Environment**
-- Tools and platforms for MLOps (e.g., Docker, Kubernetes)
-- Creating reproducible environments with containers
-- Overview of cloud service providers for machine learning
- **Version Control and Collaboration**
-- Introduction to Git and version control for data scientists
-- Managing code, data, and model versions
-- Best practices for collaborative development
- **Continuous Integration and Continuous Deployment (CI/CD)**
-- Principles of CI/CD in machine learning
-- Setting up automated testing for ML models
-- Deploying models to production environments
- **Automated Data Pipelines**
-- Building and maintaining data pipelines
-- Data validation and monitoring
-- Integrating ETL processes with machine learning workflows
- **Model Development and Testing**
-- Unit testing and integration testing for ML code
-- Experimentation frameworks for ML models
-- Ensuring reproducibility and traceability in experiments
- **Monitoring and Logging in ML Systems**
-- Techniques for monitoring models in production
-- Logging best practices for data and models
-- Tools for real-time analytics and dashboards
- **Scaling Machine Learning Operations**
-- Managing and scaling resources for ML tasks
-- Optimizing performance and cost in ML workflows
-- Use cases for serverless architectures in ML
- **Security and Compliance**
-- Securing machine learning pipelines and models
-- Managing sensitive data in ML workflows
-- Compliance with data protection regulations (e.g., GDPR, CCPA)
- **Case Studies and Industry Best Practices**
-- Review of real-world MLOps case studies
-- Common challenges and solutions in DevOps for ML
-- Future trends and emerging technologies in MLOps
Taught by
Tags
On-Demand
